As Internet of Things (IoT) is overpopulated with multitude of objects, services and interactions, efficiently locating the most relevant object is emerging as a major obstacle. Over the last few years, the Social Internet of Things (SIoT) paradigm, where objects independently establish social relationships among them, has become more popular as it provides a number of exciting characteristics to boost network navigability and carryout reliable discovery approaches. Given a large scale deployment of socially connected objects, finding the shortest path to reach the service provider remains as a fundamental challenge. In most of the existing search techniques, the physical significance of the objects is not very well explained and the geographical location of mobile objects is not considered. In this paper, to improve the search performance over the SIoT, we propose a novel object search mechanism based on physical location proximity and social context of users in social communities. The results show an enhancement over the existing search technique in terms of average path length.
